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For diagnostic purposes, the nine symptoms that compose the DSM-5 criteria for major depressive disorder (MDD) are assumed to be interchangeable indicators of one underlying disorder, implying that they should all have similar risk factors. The present study investigates this hypothesis, using a population cohort that shifts from low to elevated depression levels. We assessed the nine DSM-5 MDD criterion symptoms (using the Patient Health Questionnaire; PHQ-9) and seven depression risk factors (personal and family MDD history, sex, childhood stress, neuroticism, work hours, and stressful life events) in a longitudinal study of medical interns prior to and throughout internship (n = 1289). We tested whether risk factors varied across symptoms, and whether a latent disease model could account for heterogeneity between symptoms. All MDD symptoms increased significantly during residency training. Four risk factors predicted increases in unique subsets of PHQ-9 symptoms over time (depression history, childhood stress, sex, and stressful life events), whereas neuroticism and work hours predicted increases in all symptoms, albeit to varying magnitudes. MDD family history did not predict increases in any symptom. The strong heterogeneity of associations persisted after controlling for a latent depression factor. The influence of risk factors varies substantially across DSM depression criterion symptoms. As symptoms are etiologically heterogeneous, considering individual symptoms in addition to depression diagnosis might offer important insights obfuscated by symptom sum scores.

Silicate and carbonate weathering reactions consume atmospheric CO2 depending on the type of weathering agents, namely carbonic (H2CO3), sulfuric (H2SO4), and nitric acids (HNO3), and have potential climate implications. However, the importance of HNO3 in weathering processes in the Himalayan glacierized basins has not been examined yet but is critical to better constrain the concomitant short (<103 years) and long‐term (>106 years) variability in the carbon cycle as it can drive negative feedback to a climate. By analyzing time‐series hydro‐geochemical data of proglacial meltwater in the Ganga headwaters of Central Himalaya, we demonstrate that the weathering rate of carbonate minerals is increased 1.06 times when the role of HNO3 is considered together with H2CO3 and H2SO4 in comparison to the role of H2CO3 and H2SO4. However, we also observe that the CO2 drawdown rate decreases 1.13 times and 1.06 times when the role of all three acids is considered in silicate and carbonate weathering reactions, respectively, compared to the CO2 drawdown rates linked to the role of H2CO3 and H2SO4. Moreover, the involvement of HNO3 in chemical weathering can reduce the inorganic global carbon sink by releasing CO2 into the ocean‐atmosphere system. We conclude that HNO3‐mediated chemical weathering reactions are important processes that alter the geologic carbon cycle of high‐altitude glacierized Himalayan catchments as well as on a global scale. Key Points H2SO4 and HNO3 acids can act as additional proton sources and influence the rates of chemical weathering and atmospheric CO2 consumption Chemical weathering mediated by H2CO3, H2SO4, and HNO3 acids can enhance the rates of weathering but reduce the rates of CO2 drawdown Sulfuric and nitric acid‐mediated chemical weathering reactions are important to better constrain geological carbon cycling

This research relied on afield experiment involving a real-world instance of corporate philanthropy to shed light on both the scope and limitations of the strategic returns to corporate social responsibility (CSR). In particular, the authors demonstrate that the impact of CSR in the real world is not only less pervasive than has been previously acknowledged but also more multifaceted than has been previously conceptualized. The findings indicated that contingent on CSR awareness, which was rather low, stakeholders did react positively to the focal company not only in the consumption domain but in the employment and investment domains as well. Stakeholder attributions regarding the genuineness of the company's motives moderated these effects. [PUBLICATION ABSTRACT]

In this trial in nondiabetic patients with insulin resistance and a recent ischemic stroke or transient ischemic attack, pioglitazone was associated with a lower risk of stroke and MI than was placebo but with a higher risk of weight gain, edema, and bone fracture. Ischemic stroke and transient ischemic attack (TIA) affect more than 14 million persons worldwide annually. 1 , 2 Affected patients are at increased risk for future cardiovascular events, 3 , 4 and prevention of these adverse outcomes is a major goal in their care. Treatment of insulin resistance represents a potential new preventive strategy that could be added to standard care after ischemic stroke or TIA. 5 Insulin resistance is nearly universal in patients with type 2 diabetes but is also present in more than 50% of patients without diabetes who have had an ischemic stroke or a TIA. 6 The presence of insulin resistance increases . . .

The Indian Summer Monsoon (ISM) and meltwater from the Himalayan are the two most important sources of water in the Indian subcontinent. However, the impact of ISM on Himalayan glaciers and subsequent stream hydrology remains largely unknown. To provide new insight into the impact of rainfall on glacial hydrology, here we present hydro‐meteorological and time‐series observations of meltwater stable water isotope compositions from the snout of the Chorabari glacier in the Upper Ganga Basin, Central Himalayas across the ablation season corresponding to 2019. We observe that rainfall events (>2 mm d−1) on the glacier enhance discharge driven by ice meltwater in River Mandakini. Energy balance calculations reveal that one of the drivers behind enhanced ice meltwater contribution could be rain‐induced melting of the glacier where rainfall on the ice surface melts the glacier producing up to 13% of the total discharge at the glacier snout. Further, rainfall on glacier surface have other control on glacial processes—for example, snow metamorphism, ice flow dynamics such as short‐term acceleration in ice speed flow, and reorganization of the englacial and subglacial drainage network—that are poorly studied and needs further investigation. We conclude rainfall events on the glacier have a complex control on mountain hydrology. This study, therefore, provides an interpretative framework that calls for additional assessments of the direct and indirect impact of rainfall in glacial hydrology. Plain Language Summary Understanding the drivers of elevated ice meltwater runoff in glacier‐fed Himalayan streams is critically important in constraining the role of climate change in glacial hydrology. The conventional thinking of elevated ice meltwater runoff in Himalayan rivers is mainly attributed to global warming. However, in this study, we find that rain events on the glacier are an additional driver of enhanced ice meltwater discharge in the glacier‐fed Himalayan streams. The additional flux of ice meltwater can be explained by the reorganization of the englacial and subglacial drainage network during rainfall events and/or rain‐induced melting of the glacier ice where raindrops falling on the ice surface are releasing sensible and latent heat by englacial cooling and freezing of rain, thereby raising ice temperatures to the melting point, in result, enhanced ice meltwater contributions. As the number of extreme rainfall events and their associated catastrophes has increased in recent decades, the observed causal relationship between ISM and enhanced ice meltwater contributions in the Himalayas is critical to better manage and predict important environmental problems such as floods in the Himalayas. Key Points An isotope mixing model was developed to quantify the contributions of rain, ice, and snow meltwater to the total river discharge We show heavy monsoonal rainfall triggers the melting of glacier ice resulting in enhanced ice meltwater discharge in streams We conclude rainfall events have complex controls on glacial hydrology/processes

Imaging of biomolecules guides our understanding of their diverse structures and functions 1 , 2 . Real-space imaging at sub-nanometre resolution using cryo-electron microscopy has provided key insights into proteins and their assemblies 3 , 4 . Direct molecular imaging of glycans—the predominant biopolymers on Earth, with a plethora of structural and biological functions 5 —has not been possible so far 6 . The inherent glycan complexity and backbone flexibility require single-molecule approaches for real-space imaging. At present, glycan characterization often relies on a combination of mass spectrometry and nuclear magnetic resonance imaging to provide insights into size, sequence, branching and connectivity, and therefore requires structure reconstruction from indirect information 7 – 9 . Here we show direct imaging of single glycan molecules that are isolated by mass-selective, soft-landing electrospray ion beam deposition and imaged by low-temperature scanning tunnelling microscopy 10 . The sub-nanometre resolution of the technique enables the visualization of glycan connectivity and discrimination between regioisomers. Direct glycan imaging is an important step towards a better understanding of the structure of carbohydrates. An imaging method combining soft-landing electrospray ion beam deposition and low-temperature scanning tunnelling microscopy resolves the structures of glycans at sub-nanometre resolution, revealing the connectivity of glycan chains and the types of linkages.

From object tracking to 3D reconstruction, RGB-Depth (RGB-D) camera networks play an increasingly important role in many vision and graphics applications. Practical applications often use sparsely-placed cameras to maximize visibility, while using as few cameras as possible to minimize cost. In general, it is challenging to calibrate sparse camera networks due to the lack of shared scene features across different camera views. In this paper, we propose a novel algorithm that can accurately and rapidly calibrate the geometric relationships across an arbitrary number of RGB-D cameras on a network. Our work has a number of novel features. First, to cope with the wide separation between different cameras, we establish view correspondences by using a spherical calibration object. We show that this approach outperforms other techniques based on planar calibration objects. Second, instead of modeling camera extrinsic calibration using rigid transformation, which is optimal only for pinhole cameras, we systematically test different view transformation functions including rigid transformation, polynomial transformation and manifold regression to determine the most robust mapping that generalizes well to unseen data. Third, we reformulate the celebrated bundle adjustment procedure to minimize the global 3D reprojection error so as to fine-tune the initial estimates. Finally, our scalable client-server architecture is computationally efficient: the calibration of a five-camera system, including data capture, can be done in minutes using only commodity PCs. Our proposed framework is compared with other state-of-the-arts systems using both quantitative measurements and visual alignment results of the merged point clouds.